利用课程学习增强时空分位数预测：经验总结
Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned
AI总结：
针对时空数据训练模型难度大、直接训练性能难保证的问题，提出融合空间、时间、分位数三类课程学习与堆叠融合模块的新范式，经实验和消融研究验证可有效提升时空分位数预测性能与学习效率。
摘要：
由于时空（ST）数据本身复杂多样的特性，在时空数据上训练模型是一个尚未解决的问题，直接在原始时空数据上训练模型难以保证性能。虽然限制训练数据的多样性可以降低训练难度，但这也会导致模型缺乏知识和信息，最终造成性能下降。为应对这一挑战，我们提出了一种创新范式，该范式整合了三种独立的课程学习形式，分别从空间、时间和分位数三个维度针对性设计。此外，我们的框架加入了一个堆叠融合模块，用于整合三类课程学习带来的多样化信息，实现强劲且全面的学习过程。我们通过大量实证评估验证了该框架的有效性，凸显其在应对复杂时空挑战方面的更优性能。我们还开展了全面的消融实验，以探究所提课程设计的有效性，并阐释其如何助力提升时空数据上的学习效率。
英文摘要：
Training models on spatio-temporal (ST) data poses an open problem due to the complicated and diverse nature of the data itself, and it is challenging to ensure the model's performance directly trained on the original ST data. While limiting the variety of training data can make training easier, it can also lead to a lack of knowledge and information for the model, resulting in a decrease in performance. To address this challenge, we presented an innovative paradigm that incorporates three separate forms of curriculum learning specifically targeting from spatial, temporal, and quantile perspectives. Furthermore, our framework incorporates a stacking fusion module to combine diverse information from three types of curriculum learning, resulting in a strong and thorough learning process. We demonstrated the effectiveness of this framework with extensive empirical evaluations, highlighting its better performance in addressing complex ST challenges. We provided thorough ablation studies to investigate the effectiveness of our curriculum and to explain how it contributes to the improvement of learning efficiency on ST data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
